Part I - (Communicate Data Findings Ford GoBike System Data)¶

by (Samy Haliem)¶

Introduction¶

Ford GoBike System Data (39.4 MB): This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area.

Preliminary Wrangling¶

In [1]:
# import all packages and set plots to be embedded inline
import pandas as pd
import numpy  as np
import matplotlib.pyplot as   plt
import seaborn as sns
import plotly.express as px
from ydata_profiling import ProfileReport
sns.set()

Load in your dataset and describe its properties through the questions below. Try and motivate your exploration goals through this section.

In [2]:
bike=pd.read_csv("fordgobike-tripdata.csv")
In [3]:
bike.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB
In [4]:
bike.shape
Out[4]:
(183412, 16)
In [5]:
bike.sample(10)
Out[5]:
duration_sec start_time end_time start_station_id start_station_name start_station_latitude start_station_longitude end_station_id end_station_name end_station_latitude end_station_longitude bike_id user_type member_birth_year member_gender bike_share_for_all_trip
102036 286 2019-02-14 17:02:21.2090 2019-02-14 17:07:07.7280 7.0 Frank H Ogawa Plaza 37.804562 -122.271738 217.0 27th St at MLK Jr Way 37.817015 -122.271761 185 Subscriber 1988.0 Male No
24661 333 2019-02-25 17:58:49.3420 2019-02-25 18:04:22.8250 15.0 San Francisco Ferry Building (Harry Bridges Pl... 37.795392 -122.394203 6.0 The Embarcadero at Sansome St 37.804770 -122.403234 6051 Subscriber 1984.0 Female No
57857 327 2019-02-21 07:53:26.9930 2019-02-21 07:58:54.8480 108.0 16th St Mission BART 37.764710 -122.419957 123.0 Folsom St at 19th St 37.760594 -122.414817 5846 Subscriber 1980.0 Female No
55068 502 2019-02-21 11:08:53.4140 2019-02-21 11:17:15.5420 369.0 Hyde St at Post St 37.787349 -122.416651 21.0 Montgomery St BART Station (Market St at 2nd St) 37.789625 -122.400811 2664 Subscriber 1988.0 Male No
47381 432 2019-02-22 08:50:39.7830 2019-02-22 08:57:52.1990 36.0 Folsom St at 3rd St 37.783830 -122.398870 15.0 San Francisco Ferry Building (Harry Bridges Pl... 37.795392 -122.394203 5344 Subscriber 1971.0 Male No
110231 723 2019-02-12 18:05:20.5450 2019-02-12 18:17:24.2270 160.0 West Oakland BART Station 37.805318 -122.294837 159.0 24th St at Market St 37.816060 -122.278244 3638 Subscriber 1988.0 Male No
62977 597 2019-02-20 16:06:01.6930 2019-02-20 16:15:58.9940 104.0 4th St at 16th St 37.767045 -122.390833 27.0 Beale St at Harrison St 37.788059 -122.391865 5967 Subscriber 1966.0 Female No
163656 447 2019-02-05 09:06:13.4330 2019-02-05 09:13:40.5750 36.0 Folsom St at 3rd St 37.783830 -122.398870 17.0 Embarcadero BART Station (Beale St at Market St) 37.792251 -122.397086 5007 Subscriber 1980.0 Male No
21490 384 2019-02-26 09:12:13.1430 2019-02-26 09:18:38.0420 66.0 3rd St at Townsend St 37.778742 -122.392741 19.0 Post St at Kearny St 37.788975 -122.403452 5489 Subscriber 1980.0 Male No
164076 765 2019-02-05 08:39:42.4930 2019-02-05 08:52:28.0540 22.0 Howard St at Beale St 37.789756 -122.394643 88.0 11th St at Bryant St 37.770030 -122.411726 4981 Subscriber 1983.0 Male No
In [6]:
bike.duplicated().sum()
Out[6]:
0
In [7]:
bike.describe()
Out[7]:
duration_sec start_station_id start_station_latitude start_station_longitude end_station_id end_station_latitude end_station_longitude bike_id member_birth_year
count 183412.000000 183215.000000 183412.000000 183412.000000 183215.000000 183412.000000 183412.000000 183412.000000 175147.000000
mean 726.078435 138.590427 37.771223 -122.352664 136.249123 37.771427 -122.352250 4472.906375 1984.806437
std 1794.389780 111.778864 0.099581 0.117097 111.515131 0.099490 0.116673 1664.383394 10.116689
min 61.000000 3.000000 37.317298 -122.453704 3.000000 37.317298 -122.453704 11.000000 1878.000000
25% 325.000000 47.000000 37.770083 -122.412408 44.000000 37.770407 -122.411726 3777.000000 1980.000000
50% 514.000000 104.000000 37.780760 -122.398285 100.000000 37.781010 -122.398279 4958.000000 1987.000000
75% 796.000000 239.000000 37.797280 -122.286533 235.000000 37.797320 -122.288045 5502.000000 1992.000000
max 85444.000000 398.000000 37.880222 -121.874119 398.000000 37.880222 -121.874119 6645.000000 2001.000000
In [8]:
bike.dtypes
Out[8]:
duration_sec                 int64
start_time                  object
end_time                    object
start_station_id           float64
start_station_name          object
start_station_latitude     float64
start_station_longitude    float64
end_station_id             float64
end_station_name            object
end_station_latitude       float64
end_station_longitude      float64
bike_id                      int64
user_type                   object
member_birth_year          float64
member_gender               object
bike_share_for_all_trip     object
dtype: object
In [9]:
bike.isnull().sum()
Out[9]:
duration_sec                  0
start_time                    0
end_time                      0
start_station_id            197
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id              197
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64
In [10]:
bike.columns
Out[10]:
Index(['duration_sec', 'start_time', 'end_time', 'start_station_id',
       'start_station_name', 'start_station_latitude',
       'start_station_longitude', 'end_station_id', 'end_station_name',
       'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type',
       'member_birth_year', 'member_gender', 'bike_share_for_all_trip'],
      dtype='object')
In [11]:
bike['bike_share_for_all_trip'].value_counts()
Out[11]:
No     166053
Yes     17359
Name: bike_share_for_all_trip, dtype: int64
In [12]:
bike['user_type'].value_counts()
Out[12]:
Subscriber    163544
Customer       19868
Name: user_type, dtype: int64
In [13]:
bike['member_gender'].value_counts()
Out[13]:
Male      130651
Female     40844
Other       3652
Name: member_gender, dtype: int64
In [14]:
bike['member_birth_year'].value_counts()
Out[14]:
1988.0    10236
1993.0     9325
1989.0     8972
1990.0     8658
1991.0     8498
          ...  
1928.0        1
1878.0        1
1930.0        1
1910.0        1
1927.0        1
Name: member_birth_year, Length: 75, dtype: int64
In [15]:
bike['start_station_name'].value_counts()
Out[15]:
Market St at 10th St                                         3904
San Francisco Caltrain Station 2  (Townsend St at 4th St)    3544
Berry St at 4th St                                           3052
Montgomery St BART Station (Market St at 2nd St)             2895
Powell St BART Station (Market St at 4th St)                 2760
                                                             ... 
Willow St at Vine St                                            9
Parker Ave at McAllister St                                     7
Palm St at Willow St                                            4
21st Ave at International Blvd                                  4
16th St Depot                                                   2
Name: start_station_name, Length: 329, dtype: int64
In [16]:
bike['end_station_name'].value_counts()
Out[16]:
San Francisco Caltrain Station 2  (Townsend St at 4th St)    4857
Market St at 10th St                                         3973
Montgomery St BART Station (Market St at 2nd St)             3647
San Francisco Ferry Building (Harry Bridges Plaza)           3368
Powell St BART Station (Market St at 4th St)                 2997
                                                             ... 
Parker Ave at McAllister St                                     9
Palm St at Willow St                                            7
16th St Depot                                                   6
21st Ave at International Blvd                                  6
Willow St at Vine St                                            5
Name: end_station_name, Length: 329, dtype: int64

Cleaning Data¶

In [17]:
## make a copy of dataframe
bike_19=bike.copy()
In [18]:
##  to change data type datetime
for col in bike_19.columns:
    
    if "time" in col:
              
        bike_19[col]=pd.to_datetime(bike_19[col])
In [19]:
## to change data type 

for cols in bike_19.columns:
    
    if "id" in cols:
        
        bike_19[cols]=bike_19[cols].astype(str)
In [20]:
bike_19.isnull().sum()
Out[20]:
duration_sec                  0
start_time                    0
end_time                      0
start_station_id              0
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id                0
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64
In [21]:
bike_19.columns
Out[21]:
Index(['duration_sec', 'start_time', 'end_time', 'start_station_id',
       'start_station_name', 'start_station_latitude',
       'start_station_longitude', 'end_station_id', 'end_station_name',
       'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type',
       'member_birth_year', 'member_gender', 'bike_share_for_all_trip'],
      dtype='object')
In [22]:
## remove unnessary columns such (start_station_latitude,start_station_longitude,end_station_latitude,end_station_longitude ,start_station_id,end_station_id )
bike_19.drop(['start_station_id','start_station_latitude','start_station_longitude','end_station_id'], axis=1, inplace=True)
In [23]:
bike_19.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 12 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             183412 non-null  int64         
 1   start_time               183412 non-null  datetime64[ns]
 2   end_time                 183412 non-null  datetime64[ns]
 3   start_station_name       183215 non-null  object        
 4   end_station_name         183215 non-null  object        
 5   end_station_latitude     183412 non-null  float64       
 6   end_station_longitude    183412 non-null  float64       
 7   bike_id                  183412 non-null  object        
 8   user_type                183412 non-null  object        
 9   member_birth_year        175147 non-null  float64       
 10  member_gender            175147 non-null  object        
 11  bike_share_for_all_trip  183412 non-null  object        
dtypes: datetime64[ns](2), float64(3), int64(1), object(6)
memory usage: 16.8+ MB
In [24]:
bike_19.drop(['end_station_latitude','end_station_longitude'], axis=1, inplace=True)
In [25]:
bike_19.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 10 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             183412 non-null  int64         
 1   start_time               183412 non-null  datetime64[ns]
 2   end_time                 183412 non-null  datetime64[ns]
 3   start_station_name       183215 non-null  object        
 4   end_station_name         183215 non-null  object        
 5   bike_id                  183412 non-null  object        
 6   user_type                183412 non-null  object        
 7   member_birth_year        175147 non-null  float64       
 8   member_gender            175147 non-null  object        
 9   bike_share_for_all_trip  183412 non-null  object        
dtypes: datetime64[ns](2), float64(1), int64(1), object(6)
memory usage: 14.0+ MB
In [26]:
## add new columns duration by minute 
bike_19['duration_min']=bike_19['duration_sec']/60
In [27]:
bike_19.sample(5)
Out[27]:
duration_sec start_time end_time start_station_name end_station_name bike_id user_type member_birth_year member_gender bike_share_for_all_trip duration_min
65239 188 2019-02-20 09:58:32.054 2019-02-20 10:01:41.031 Post St at Kearny St Montgomery St BART Station (Market St at 2nd St) 6595 Subscriber 1971.0 Male No 3.133333
5074 392 2019-02-28 14:08:18.337 2019-02-28 14:14:50.668 The Embarcadero at Sansome St Davis St at Jackson St 5031 Subscriber 1989.0 Female No 6.533333
167804 699 2019-02-04 16:45:05.532 2019-02-04 16:56:44.867 4th St at Mission Bay Blvd S Beale St at Harrison St 2758 Subscriber 1982.0 Male No 11.650000
139514 1063 2019-02-07 20:47:15.498 2019-02-07 21:04:58.895 23rd St at Tennessee St 20th St at Bryant St 5145 Subscriber 1985.0 Male No 17.716667
181947 839 2019-02-01 08:37:20.847 2019-02-01 08:51:20.561 Telegraph Ave at 19th St Emeryville Public Market 4476 Subscriber 1988.0 Male No 13.983333
In [28]:
bike_19.isnull().sum()
Out[28]:
duration_sec                  0
start_time                    0
end_time                      0
start_station_name          197
end_station_name            197
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
duration_min                  0
dtype: int64
In [29]:
## remove null values from start_station_name and end_station_name 
bike_19.dropna(subset='start_station_name',inplace=True)
In [30]:
bike_19.isnull().sum()
Out[30]:
duration_sec                  0
start_time                    0
end_time                      0
start_station_name            0
end_station_name              0
bike_id                       0
user_type                     0
member_birth_year          8263
member_gender              8263
bike_share_for_all_trip       0
duration_min                  0
dtype: int64
In [31]:
## add new column member_age by to specify the member age
bike_19['member_age']=2019-bike_19['member_birth_year']
In [32]:
bike_19.sample(5)
Out[32]:
duration_sec start_time end_time start_station_name end_station_name bike_id user_type member_birth_year member_gender bike_share_for_all_trip duration_min member_age
91964 1827 2019-02-15 20:35:56.273 2019-02-15 21:06:23.341 4th St at 16th St 24th St at Chattanooga St 2567 Subscriber 1996.0 Female No 30.450000 23.0
152138 1207 2019-02-06 16:12:01.999 2019-02-06 16:32:09.506 Montgomery St BART Station (Market St at 2nd St) 17th St at Valencia St 3284 Subscriber NaN NaN No 20.116667 NaN
90294 632 2019-02-16 13:04:57.632 2019-02-16 13:15:30.303 Market St at Franklin St Market St at Dolores St 1596 Customer 1984.0 Other No 10.533333 35.0
151282 732 2019-02-06 17:19:40.051 2019-02-06 17:31:52.929 Union Square (Powell St at Post St) Rhode Island St at 17th St 4626 Subscriber 1970.0 Male No 12.200000 49.0
38283 767 2019-02-23 12:15:18.239 2019-02-23 12:28:05.466 Union Square (Powell St at Post St) Laguna St at Hayes St 6292 Subscriber 1995.0 Male Yes 12.783333 24.0
In [33]:
bike_19['member_age'].value_counts()
Out[33]:
31.0     10214
26.0      9323
30.0      8967
29.0      8640
28.0      8484
         ...  
91.0         1
141.0        1
89.0         1
109.0        1
92.0         1
Name: member_age, Length: 75, dtype: int64
In [34]:
px.bar(data_frame=bike_19['member_age'].value_counts(),title="member age")
In [35]:
px.histogram(data_frame=bike_19,x='member_age',title="member age")
In [36]:
## as we see there is lot of outliers ,i should remove it i think 70 years is enough 

bike_19.drop('member_birth_year', axis=1, inplace=True)
In [37]:
bike_19.sample(10)
Out[37]:
duration_sec start_time end_time start_station_name end_station_name bike_id user_type member_gender bike_share_for_all_trip duration_min member_age
10739 596 2019-02-27 19:43:17.045 2019-02-27 19:53:13.312 Valencia St at Clinton Park Garfield Square (25th St at Harrison St) 5469 Subscriber Male No 9.933333 23.0
68222 789 2019-02-20 05:21:28.576 2019-02-20 05:34:37.715 McAllister St at Baker St 1st St at Folsom St 6516 Subscriber Female No 13.150000 31.0
64179 787 2019-02-20 12:40:36.858 2019-02-20 12:53:44.187 Doyle St at 59th St Doyle St at 59th St 828 Customer NaN No 13.116667 NaN
71084 528 2019-02-19 17:44:42.828 2019-02-19 17:53:31.297 San Francisco Caltrain (Townsend St at 4th St) Bryant St at 15th St 4624 Subscriber Male No 8.800000 30.0
132217 672 2019-02-09 09:01:07.710 2019-02-09 09:12:20.300 West St at 40th St Telegraph Ave at 19th St 3131 Subscriber Male No 11.200000 29.0
119244 145 2019-02-11 18:06:43.106 2019-02-11 18:09:08.939 11th St at Bryant St Division St at Potrero Ave 1809 Subscriber Male No 2.416667 36.0
73382 1069 2019-02-19 13:47:11.539 2019-02-19 14:05:01.535 S Van Ness Ave at Market St The Embarcadero at Sansome St 5335 Subscriber Male No 17.816667 28.0
179244 457 2019-02-01 14:43:03.081 2019-02-01 14:50:40.523 Union Square (Powell St at Post St) Townsend St at 5th St 4445 Subscriber Male No 7.616667 23.0
85440 2514 2019-02-17 14:37:58.646 2019-02-17 15:19:53.523 Esprit Park Lombard St at Columbus Ave 5874 Subscriber Female No 41.900000 25.0
163488 977 2019-02-05 09:06:38.631 2019-02-05 09:22:56.546 Webster St at Grove St Hubbell St at 16th St 1755 Subscriber Male No 16.283333 46.0
In [38]:
bike_19=bike_19.query("member_age<=70")
In [39]:
px.histogram(data_frame=bike_19,x='member_age',title="member age")
In [40]:
px.box(data_frame=bike_19,y='member_age',title="member age")
In [41]:
bike_19['member_age'].describe()
Out[41]:
count    174377.000000
mean         34.038876
std           9.714521
min          18.000000
25%          27.000000
50%          32.000000
75%          39.000000
max          70.000000
Name: member_age, dtype: float64
In [42]:
bike_19.isnull().sum()
Out[42]:
duration_sec               0
start_time                 0
end_time                   0
start_station_name         0
end_station_name           0
bike_id                    0
user_type                  0
member_gender              0
bike_share_for_all_trip    0
duration_min               0
member_age                 0
dtype: int64
In [ ]:
In [43]:
bike_19.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174377 entries, 0 to 183411
Data columns (total 11 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174377 non-null  int64         
 1   start_time               174377 non-null  datetime64[ns]
 2   end_time                 174377 non-null  datetime64[ns]
 3   start_station_name       174377 non-null  object        
 4   end_station_name         174377 non-null  object        
 5   bike_id                  174377 non-null  object        
 6   user_type                174377 non-null  object        
 7   member_gender            174377 non-null  object        
 8   bike_share_for_all_trip  174377 non-null  object        
 9   duration_min             174377 non-null  float64       
 10  member_age               174377 non-null  float64       
dtypes: datetime64[ns](2), float64(2), int64(1), object(6)
memory usage: 16.0+ MB
In [ ]:
In [44]:
## add new column s_day,s_hour 
bike_19["s_day"] = bike_19["start_time"].dt.day_name()
bike_19["s_hour"] = bike_19["start_time"].dt.hour
In [45]:
bike_19.sample()
Out[45]:
duration_sec start_time end_time start_station_name end_station_name bike_id user_type member_gender bike_share_for_all_trip duration_min member_age s_day s_hour
74621 358 2019-02-19 10:01:24.636 2019-02-19 10:07:22.641 Haste St at Telegraph Ave University Ave at Oxford St 6444 Subscriber Male Yes 5.966667 22.0 Tuesday 10
In [46]:
bike_19.dtypes
Out[46]:
duration_sec                        int64
start_time                 datetime64[ns]
end_time                   datetime64[ns]
start_station_name                 object
end_station_name                   object
bike_id                            object
user_type                          object
member_gender                      object
bike_share_for_all_trip            object
duration_min                      float64
member_age                        float64
s_day                              object
s_hour                              int64
dtype: object
In [47]:
bike_19.drop('duration_sec', axis=1, inplace=True)
In [48]:
bike_19['duration_min']=bike_19['duration_min'].astype(int)
In [49]:
bike_19['member_age']=bike_19['member_age'].astype(int)
In [50]:
bike_19.dtypes
Out[50]:
start_time                 datetime64[ns]
end_time                   datetime64[ns]
start_station_name                 object
end_station_name                   object
bike_id                            object
user_type                          object
member_gender                      object
bike_share_for_all_trip            object
duration_min                        int32
member_age                          int32
s_day                              object
s_hour                              int64
dtype: object
In [51]:
bike_19.set_index('bike_id', inplace=True)
In [52]:
bike_19.sample()
Out[52]:
start_time end_time start_station_name end_station_name user_type member_gender bike_share_for_all_trip duration_min member_age s_day s_hour
bike_id
4759 2019-02-12 11:09:01.071 2019-02-12 11:22:34.881 Steuart St at Market St Bryant St at 6th St Subscriber Male No 13 36 Tuesday 11
In [53]:
## store the cleand data to csv file
bike_19.to_csv("cleand_fordgobike-tripdata.csv",index=False)
In [54]:
bike_19=pd.read_csv("cleand_fordgobike-tripdata.csv")

What is the structure of your dataset?¶

The original combined data set contains approximately 183,412 individual trip records. There are 16 variables in this data set, which can be divided into three major categories:

Trip duration: This category includes the duration_sec, start_time, and end_time variables. These variables provide information about the length of each trip, the time the trip started, and the time the trip ended. Station information: This category includes the start_station_id, start_station_name, start_station_latitude, start_station_longitude, end_station_id, end_station_name, end_station_latitude, and end_station_longitude variables. These variables provide information about the start and end stations for each trip. Member information (anonymized): This category includes the bike_id, user_type, member_birth_year, member_gender, and bike_share_for_all_trip variables. These variables provide information about the bike used for each trip, the user type (member or casual rider), the member's birth year, the member's gender, and whether the member has a bike share pass for all trips. In addition to the original variables, the following derived features were created to assist with exploration and analysis:

Trip information: The duration_min variable was created by dividing the duration_sec variable by 60. The s_day, and s_hour, were created by extracting the corresponding information from the start_time variable. Member: The member_age variable was created by calculating the age of the member based on their birth year.

What is/are the main feature(s) of interest in your dataset?¶

I am interested in exploring the patterns of bike trips in terms of their duration and rental events, and how these patterns relate to the riders' characteristics, such as their user type, gender, and age. i want to get a sense of how and what people are using the bike-sharing service for.

Some specific questions you want to answer include:

When are most trips taken? In terms of time of day, day of the week? How long does the average trip take? Do these patterns differ depending on whether the user is a subscriber or a customer? I can answer these questions by conducting a statistical analysis of the data. I can use descriptive statistics to summarize the data,

For example, I could use a histogram to visualize the distribution of trip durations. I could also describe the difference in the average trip duration between subscribers and customers.

By analyzing the data in this way, I can gain a deeper understanding of how people are using the bike-sharing service. This information can be used to improve the service and make it more user-friendly.

Here are some additional questions I could explore:

What are the most popular start and end stations? Do people tend to take longer trips during the weekend? Are there any differences in trip patterns between men and women? Do younger people tend to take shorter trips? By asking these questions and exploring the data, I can learn more about how people are using the bike-sharing service and how it can be improved.

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

The start date/time and duration of each trip can be used to understand how long a trip typically takes and when it is most likely to occur. The user information, such as user type, gender, and age, can be used to identify the main target customer groups. By summarizing the bike usage data for different groups of riders, we can see if there are any special patterns associated with a specific group.

For example, we might find that subscribers tend to take longer trips than customers, or that men tend to take more trips than women. We might also find that younger people tend to take shorter trips than older people.

This information can be used to improve the bike-sharing service by targeting different groups of riders with different marketing messages. For example, we might target subscribers with messages about longer trips, or we might target women with messages about safety.

Here are some specific questions you could ask:

What is the average trip duration for subscribers? For customers? What are the most popular times of day for trips? For weekdays? For weekends? What are the most popular start and end stations? Do men or women take more trips? Do younger or older people take more trips? Do subscribers or customers take longer trips? By asking these questions and exploring the data, you can learn more about how people are using the bike-sharing service and how it can be improved.

Univariate Exploration¶

In [55]:
bike_19.sample(7)
Out[55]:
start_time end_time start_station_name end_station_name user_type member_gender bike_share_for_all_trip duration_min member_age s_day s_hour
151834 2019-02-05 17:44:39.038 2019-02-05 17:53:52.199 Powell St BART Station (Market St at 5th St) 8th St at Ringold St Subscriber Male No 9 38 Tuesday 17
174070 2019-02-01 07:08:00.713 2019-02-01 07:18:22.040 Howard St at Beale St The Embarcadero at Vallejo St Subscriber Female No 10 45 Friday 7
85057 2019-02-16 15:05:39.221 2019-02-16 15:14:50.254 Hearst Ave at Euclid Ave North Berkeley BART Station Subscriber Male No 9 35 Saturday 15
108670 2019-02-12 09:44:18.766 2019-02-12 09:50:57.895 Vine St at Shattuck Ave Hearst Ave at Euclid Ave Subscriber Male No 6 30 Tuesday 9
11318 2019-02-27 18:19:51.308 2019-02-27 18:22:40.937 Harrison St at 17th St 19th St at Florida St Subscriber Male No 2 39 Wednesday 18
154262 2019-02-05 12:28:21.080 2019-02-05 12:29:41.289 4th St at 16th St 4th St at Mission Bay Blvd S Subscriber Male No 1 20 Tuesday 12
11554 2019-02-27 18:05:38.696 2019-02-27 18:07:38.393 Frank H Ogawa Plaza MLK Jr Way at 14th St Subscriber Male No 1 31 Wednesday 18
In [56]:
bike_19.describe()
Out[56]:
duration_min member_age s_hour
count 174377.000000 174377.000000 174377.000000
mean 11.245692 34.038876 13.458260
std 27.404801 9.714521 4.736039
min 1.000000 18.000000 0.000000
25% 5.000000 27.000000 9.000000
50% 8.000000 32.000000 14.000000
75% 13.000000 39.000000 17.000000
max 1409.000000 70.000000 23.000000
In [ ]:
In [57]:
## creating a function that carries the visualizations titles,x_label and y_label
def plot_label(title,x_label,y_label):
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.xticks(rotation=45)
In [58]:
## How does the trip distribution vary by day of week?
color_base=sns.color_palette()[0]
sns.countplot(data=bike_19,x='s_hour',color=color_base)
plot_label("Trip Start Hour Of The Day","Hour Of Day","Count")
plt.show();
In [59]:
## What are the most common destinations for trips on each day of the week?
days = ["Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"]
sns.countplot(data=bike_19,x='s_day',color=color_base,order=days)
plot_label("Trip Start Day Of The Week ","Day Of The Week","Count")

The number of bike trips peaks during two time periods: 8 am-9 am and 5 pm-6 pm. These are the typical rush hours when people are commuting to and from work or school. The fact that the majority of rides happen on weekdays also supports the idea that bike sharing is primarily used for commuting.¶

In [60]:
## What is the characteristics of the riders and members to better understand their composition?
In [61]:
sns.countplot(data=bike_19,x='user_type',color=color_base)
plot_label("Member Type","User Type","Count");
In [62]:
sns.countplot(data=bike_19,x='member_gender',color=color_base)
plot_label("Member Gender","Gender","Count")
In [63]:
sns.countplot(data=bike_19,x='bike_share_for_all_trip',color=color_base)
plot_label("Bike Share Trip","Bike Share For All Trip","Count");
In [64]:
bins=np.arange(0,bike_19["member_age"].max()+5,5)
plt.hist(data=bike_19,x="member_age",bins=bins)
plot_label("Member Age","Age","count")

Based on the data, it appears that most riders were male subscribers who did not use bike share for all trips. The majority of members were between 20 and 40 years old, which corresponds to the trip distribution plots that show most trips occurred on weekdays and during rush hours. Additionally, as age increased, bike usage dropped significantly.¶

In [65]:
bike_19["duration_min"].describe([.99])
Out[65]:
count    174377.000000
mean         11.245692
std          27.404801
min           1.000000
50%           8.000000
99%          52.000000
max        1409.000000
Name: duration_min, dtype: float64
In [66]:
fig = px.histogram(
    data_frame=bike_19,
    x='duration_min',
   
    title='Distribution of Trip Durations'
)

fig.update_layout(
    xaxis_title='Duration (minutes)',
    yaxis_title='Count',
    xaxis_range=[0, 70]
)

fig.show()

Based on the data, it appears that the majority of trips (around 99%) had a duration of less than 1 hour. Most of these trips were observed to fall within the 5 to 10-minute range.¶

In [ ]:

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

The number of trips peaked around 8-9 am and 5-6 pm on work days (Mon-Fri) compared to weekends.

In terms of user demographics, there were more male riders than female, and most members were subscribers compared to casual riders. The majority of the members did not use bike share for all of their trips and were between 20 and 40 years old.

Most rides were quick and short, lasting between 5 to 10 minutes. However, there were some very long outliers like 24 hours. Luckily, no transformation was needed due to the straightforwardness of the data.

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

To focus on the typical use of the system, the outlier long trip records were filtered out using .query(). The distribution of trip duration was initially unclear but became much clearer after using .describe() and xticks() to zoom in to a smaller range where most trip records fell.

Bivariate Exploration¶

In this section, investigate relationships between pairs of variables in your data. Make sure the variables that you cover here have been introduced in some fashion in the previous section (univariate exploration).

In [67]:
### How does the trip duration distribution differ between customers and subscribers?
In [68]:
sns.violinplot(data=bike_19.query('duration_min<=60'), x='user_type', y='duration_min', color=color_base,inner='quartail')
plot_label("trip duration distribution",'User Type','Trip Duration in Minutes')

The trip duration distribution is much narrower for subscribers compared to casual riders on the shorter/quicker trip end overall. It seems like subscribers have a more specific usage or targeted goal riding the bikes compared to customers who vary more and generally rented the bikes for longer.¶

In [ ]:
In [69]:
### How does the trip duration distribution differ by gender?
In [70]:
fig = px.box(
    data_frame=bike_19.query('duration_min<=60'),
    x='member_gender',
    y='duration_min',
    title='Distribution of Trip Durations by Gender'
)

fig.update_layout(
    xaxis_title='Gender',
    yaxis_title='Duration (minutes)'
)

fig.show()

Male riders tend to have shorter trips compared to female users, as indicated by both a smaller median and shorter IQR.Ӧ

In [71]:
#### Is the average duration of a trip different for different days of the week? If so,
#### which days have the longest and shortest average durations?
In [72]:
sns.barplot(data=bike_19, 
            x="s_day", 
            y="duration_min",
            order=days,
            color=color_base)

plot_label("Trip Duration by Day of Week","Day of Week",'Average Trip Duration (minutes)')

Riding trips are much shorter on Monday through Friday compared to weekends. This indicates a pretty stable and efficient usage of the sharing system on normal workdays, while more casual and flexible use on weekends¶

In [ ]:
In [73]:
#### What is the average age of members on the week?
In [74]:
sns.barplot(data=bike_19,x="s_day",y="member_age",color=color_base,order=days)
plot_label("AVG member age  due to the day of the week","Day Of The Week","Age");

Riders who rent bikes Monday through Friday are slightly older than those who ride on weekends. This corresponds to the commute to work usage that was observed from the above univariable exploration plots.¶

In [75]:
#### What is the distribution of trips by hour of the day for Coustomer users and Subscribe users?
In [76]:
sns.countplot(data=bike_19,x='s_hour',hue='user_type')
plot_label("Hour of the day vs user_type","Hour Of Day","Count");

Subscriber usage clearly peaks during typical rush hours when people go to work in the morning and get off work in the afternoon. This double confirms their usage purpose and goal of riding. A similar pattern was not observed among customers who tend to ride most in the afternoon or early evening for a different purpose than the subscriber riders¶

In [ ]:
In [77]:
### What is the distribution of trips by Day of the week for Coustomer users and Subscribe users?
In [78]:
sns.countplot(data=bike_19,x='s_day',hue='user_type',order=days)
plot_label("weekly day vs user_type","User Type","Count");

There was much more subscriber usage than casual customers overall. The drop in volume on weekends for subscribers indicates that they primarily ride bikes for work commutes during workdays. Conversely, the slight increase in use for customers on weekends demonstrates that the use was more for leisure/touring and relaxing purposes.¶

In [ ]:
In [ ]:
In [79]:
### What is the distribution of Age of  Coustomer users and Subscribe users?
In [80]:
fig = px.box(data_frame=bike_19, x="user_type", y="member_age")

fig.update_layout(
    title="Member Age Distribution by User Type",
    yaxis_title="Member Age"
)

fig.show()

In line with the Member age by weekdays plot, it can be observed that subscribers who predominantly ride bikes from Monday to Friday tend to be slightly older compared to customers. Additionally, subscribers exhibit a broader range of ages as compared to customers.¶

In [ ]:

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

The bike-sharing system is used more by subscribers than customers. Subscribers tend to use the system for work commutes, resulting in most trips being on work days (Mon-Fri) and especially during rush hours (when going to work in the morning and getting off work in the afternoon). On the other hand, customers tend to ride for fun in the afternoon or early evenings over weekends.

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Subscribers are slightly older than customers on average but take much shorter/quicker rides.

Multivariate Exploration¶

Create plots of three or more variables to investigate your data even further. Make sure that your investigations are justified, and follow from your work in the previous sections.

In [81]:
### How does the average trip duration vary between customers and subscribers on weekdays?
In [82]:
sns.pointplot(data=bike_19,x="s_day",y="duration_min",hue="user_type",dodge=.3,order=days)
plot_label("the average trip duration vary between customers and subscribers on weekdays","s_day","AVG Trip Duration In Minute ")

Subscribers ride much shorter/quicker trips compared to customers on each day of the week, as seen from the above plot. Both user types have an obvious increase in trip duration on Saturdays and Sundays over weekends, especially for casual riders. Subscribers usage seems to be more efficient than customers overall and maintained a very consistent average duration Monday through Friday¶

In [ ]:
In [ ]:

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

The multivariate exploration strengthened some of the patterns discovered in the previous bivariate exploration as well as univariate exploration. The relationship between the multiple variables plotted is visualized altogether and information is presented combined. The efficient/short period of usage for subscribers corresponds to their high concentration on rush hours Monday through Friday, indicating that the use is primarily for work commutes. The more relaxing and flexible pattern of customer use shows that they’re taking advantage of the bike-sharing system quite differently from the subscribers, heavily over weekends and in the afternoon, probably for city tours or leisure purposes.

Were there any interesting or surprising interactions between features?¶

The interactions between features are all supplementing each other and make sense when looked at combined; there’s no big surprise observed. The usage habit difference between male and females is not that much or obvious throughout the exploration, which could be related to the imbalanced number of female riders/records compared to male ones. It would be interesting to see how males and females use the system differently if there were more female data.

In [ ]: